Automatic Detection and Classification of Microcalcification, Mass, Architectural Distortion and Bilateral Asymmetry in Digital Mammogram

Authors: S. Shanthi, V. Muralibhaskaran

Abstract:

Mammography has been one of the most reliable methods for early detection of breast cancer. There are different lesions which are breast cancer characteristic such as microcalcifications, masses, architectural distortions and bilateral asymmetry. One of the major challenges of analysing digital mammogram is how to extract efficient features from it for accurate cancer classification. In this paper we proposed a hybrid feature extraction method to detect and classify all four signs of breast cancer. The proposed method is based on multiscale surrounding region dependence method, Gabor filters, multi fractal analysis, directional and morphological analysis. The extracted features are input to self adaptive resource allocation network (SRAN) classifier for classification. The validity of our approach is extensively demonstrated using the two benchmark data sets Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammograph (DDSM) and the results have been proved to be progressive.

Keywords: Feature extraction, fractal analysis, Gabor filters, multiscale surrounding region dependence method, SRAN.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1337615

References:

[1] American Cancer Society, “Glopbal Cancer Facts & Figures”, American Cancer Society, Atlanda, 2011.
[2] International Agency for Research on Cancer IARC, World Cancer Report 2008: IARC.
[3] S.Banik, R.M. Rangayyan, and J.E.L. Desautels, “Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms”, International Journal of Computer Assisted Radiology and Surgery, vol. 8, pp. 121–134, 2013.
[4] S. Shanthi and V. Murali Bhaskaran, “Intuitionistic fuzzy c-means and decision tree approach for breast cancer detection and classification”, European Journal of Scientific Research, vol. 66, no.3, pp. 345-351, 2011.
[5] S. Shanthi and V. Murali Bhaskaran, “A Novel Approach for Detecting and Classifying Breast Cancer in Mammogram Images”, International Journal of Intelligent Information Technologies, vol. 9, no. 1, pp. 21-39, 2013.
[6] S. Shanthi and V. Murali Bhaskaran, “Computer aided system for detection and classification of breast cancer”, International Journal of Information Technology, Control and Automation, vol. 2, no.4, pp. 87- 98, 2012.
[7] V. Nguyen, "An Automated Method to Segment and Classify Masses in Mammograms", World Academy of Science, Engineering and Technology, vol. 3, no. 4, pp. 761 – 766, 2009.
[8] A. Sindhuja and V. Sadasivam, “Automatic Detection of Breast Tumors in Sonoelastographic Images Using DWT”, World Academy of Science, Engineering and Technology, vol. 7, no. 9, pp. 596 – 602, 2013.
[9] S. Banik, R.M. Rangayyan and J.E.L. Desautels, “Detection of architectural distortion in prior mammograms”, IEEE Transactions on Medical Imaging, vol. 30, no. 2, pp. 279-294, 2011.
[10] M. Karnan and K. Thangavel, “Automatic detection of the breast border and nipple positionon digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications”, Computer Methods and Programs in Biomedicine, vol. 87, pp. 12–20, 2007.
[11] T. Matsubara, T. Ichikawa, T. Hara, H. Fujita, S. Kasai, T. Endo and T. Iwase, “Automated detection methods for architectural distortions around skin line and within mammary gland on mammograms”, International Congress Series, vol. 1256, pp. 950–955, 2003.
[12] A. Oliver, A. Torrent, X. Llado, M. Tortajada, L. Tortajada, M. Sentis, J. Freixenet, and R. Zwiggelaar, “Automatic microcalcification and cluster detection for digital and digitised mammograms”, Knowledge-Based Systems, vol. 28, pp. 68–75, 2012.
[13] R.M. Rangayyan, R.J. Ferrari, and A.F. Frere, “Analysis of bilateral asymmetry in mammograms using directional, morphological, and density features”, Journal of Electronic Imaging, vol. 16, no. 1, pp. 013003-1-013003-12, 2007.
[14] A.N. Karahaliou, I.S. Boniatis, S.G. Skiadopoulos, F.N. Sakellaropoulos, N.S. Arikidis, E.A. Likaki, G.S. Panayiotakis, and L.I. Costaridou, “Breast cancer diagnosis: analyzing texture of tissue surrounding microcalcifications”, IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 6, pp. 731– 738,2008.
[15] J.K. Kim, and H.W. Park, “Statistical textural features for detection of microcalcifications in digitized mammograms”, IEEE Transactions on Medical Imaging, vol. 18, no. 3, pp. 231-238, 1999.
[16] A. Mencattini, M. Salmeri, G. Rabottino, and S. Salicone, “Metrological characterization of a CADx system for the classification of breast masses in mammograms”, IEEE Transactions on Instrumentation and Measurement, vol. 59, no. 11, pp. 2792–2799, 2010.
[17] A. Tiedeu, C. Daul, A. Kentsop, P. Graebling, and D. Wolf, “Texturebased analysis of clustered microcalcifications detected on mammograms”, Digital Signal Processing, vol. 22, no. 1, pp. 124–132, 2012.
[18] S. Shanthi and V. Murali Bhaskaran, “A Novel Approach for Classification of Abnormalities in Digitized Mammograms”, Sadhana - Academy Proceedings in Engineering Science journal, vol. 39, no. 5, pp. 1141–1150, 2014.
[19] T. Mu, A.K. Nandi, and R.M. Rangayyan, “Classification of breast masses using selected shape, edge-sharpness, and texture features with linear and kernel-based classifiers”, Journal of Digital Imaging, vol. 21, no. 2, pp. 153-169, 2008.
[20] L de O. Martins, G.B. Junior, A.C. Silva, A.C.D.E. Paiva, and M. Gattass, “Detection of Masses in Digital Mammograms using K-means and Support Vector Machine”, Electronic Letters on Computer Vision and Image Analysis, vol. 8, no. 2, pp. 39-50, 2009.
[21] M. Nemoto, S. Honmura, A. Shimizu, D. Furukawa, H. Kobatake, and S. Nawano, “A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows”, International Journal of Computer Assisted Radiology and Surgery, vol. 4, no. 1, pp. 27–36, 2009.
[22] R.M. Rangayyan, S.Banik, and J.E.L. Desautels, “Computer-aided detection of architectural distortion in prior mammograms of interval cancer”, Journal of Digital Imaging, vol. 23, no. 5, pp. 611-631, 2010.
[23] American College of Radiology, 1998, “Illustrated Breast Imaging Reporting and Data System BI-RADS”, American College of Radiology, Reston, PA, 3rd edition, 1998.
[24] R.M. Haralick, K. Shanmugan, and I. Dinstein, “Textural features for image classification”, IEEE Transactions of System, Man, Cybernetics, SMC-3, pp. 610–621, 1973.
[25] R. Lopes and N. Betrouni, “Fractal and multifractal analysis: a review”, Medical Image Analysis, vol. 13, pp. 634–649, 1999.
[26] W. Kinsner, “A unified approach to fractal dimensions”, International Journal of Cognitive Informatics and Natural Intelligence, vol.1, pp. 26- 46, 2007.
[27] J. Suckling, J. Parker, D.R. Dance, S. Astley, I. Hutt, C. Boggis, I. Ricketts, E. Stamatakis, N. Cerneaz, SL. Kok, P. Taylor, D. Betal, and J. Savage, “The Mammographic Image Analysis Society digital mammogram database”, Proceeding of International Workshop on Digital Mammography, pp. 211–221,1994.
[28] M. Heath, K. Bowyer, D. Kopans, R. Moor, and W.P. Kegelmeyer, Proceedings of the Fifth International Workshop on Digital Mammography, M.J. Yaffe, ed. pp. 212-218, Medical Physics Publishing, ISBN 1-930524-00-5, 2001.